Search results
Results from the WOW.Com Content Network
To process this statement without an index the database software must look at the last_name column on every row in the table (this is known as a full table scan). With an index the database simply follows the index data structure (typically a B-tree) until the Smith entry has been found; this is much less computationally expensive than a full ...
The cost is predictable, as every time database system needs to scan full table row by row. When table is less than 2 percent of database block buffer, the full scan table is quicker. Cons: Full table scan occurs when there is no index or index is not being used by SQL. And the result of full scan table is usually slower that index table scan.
Chi Index [50] is an external validation index that measure the clustering results by applying the chi-squared statistic. This index scores positively the fact that the labels are as sparse as possible across the clusters, i.e., that each cluster has as few different labels as possible.
DD/XL is an add-in for Microsoft Excel that adds Data Desk Functionality directly to the Spreadsheet. Data Desk's developer, Data Description, pioneered linked graphic displays including a 3-D rotating plot and graphical slider control of parameters. It has also developed proprietary technology for computer-based multimedia instruction and ...
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
For a guide to displaying mathematical equations and formulas, see Help:Displaying a formula; For a guide to editing, see Wikipedia:Contributing to Wikipedia; For an overview of commonly used style guidelines, see Wikipedia:Simplified Manual of Style; For a page on how to use Wikipedia in bite-sized morsels, see Wikipedia:Tips
The Dunn index (DI) (introduced by J. C. Dunn in 1974) is a metric for evaluating clustering algorithms. [ 1 ] [ 2 ] This is part of a group of validity indices including the Davies–Bouldin index or Silhouette index , in that it is an internal evaluation scheme, where the result is based on the clustered data itself.
Consider a set of points in some space to be clustered. Let ε be a parameter specifying the radius of a neighborhood with respect to some point. For the purpose of DBSCAN clustering, the points are classified as core points, (directly-) reachable points and outliers, as follows: